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<li><a class="reference internal" href="#">Classifier Chain</a></li>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-multioutput-plot-classifier-chain-yeast-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
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<div class="sphx-glr-example-title section" id="classifier-chain">
<span id="sphx-glr-auto-examples-multioutput-plot-classifier-chain-yeast-py"></span><h1>Classifier Chain<a class="headerlink" href="#classifier-chain" title="Permalink to this headline">¶</a></h1>
<p>Example of using classifier chain on a multilabel dataset.</p>
<p>For this example we will use the <a class="reference external" href="https://www.openml.org/d/40597">yeast</a> dataset which contains
2417 datapoints each with 103 features and 14 possible labels. Each
data point has at least one label. As a baseline we first train a logistic
regression classifier for each of the 14 labels. To evaluate the performance of
these classifiers we predict on a held-out test set and calculate the
<a class="reference internal" href="../../modules/model_evaluation.html#jaccard-similarity-score"><span class="std std-ref">jaccard score</span></a> for each sample.</p>
<p>Next we create 10 classifier chains. Each classifier chain contains a
logistic regression model for each of the 14 labels. The models in each
chain are ordered randomly. In addition to the 103 features in the dataset,
each model gets the predictions of the preceding models in the chain as
features (note that by default at training time each model gets the true
labels as features). These additional features allow each chain to exploit
correlations among the classes. The Jaccard similarity score for each chain
tends to be greater than that of the set independent logistic models.</p>
<p>Because the models in each chain are arranged randomly there is significant
variation in performance among the chains. Presumably there is an optimal
ordering of the classes in a chain that will yield the best performance.
However we do not know that ordering a priori. Instead we can construct an
voting ensemble of classifier chains by averaging the binary predictions of
the chains and apply a threshold of 0.5. The Jaccard similarity score of the
ensemble is greater than that of the independent models and tends to exceed
the score of each chain in the ensemble (although this is not guaranteed
with randomly ordered chains).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Adam Kleczewski</span>
<span class="c1"># License: BSD 3 clause</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
<span class="kn">from</span> <span class="nn">sklearn.multioutput</span> <span class="kn">import</span> <span class="n">ClassifierChain</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.multiclass</span> <span class="kn">import</span> <span class="n">OneVsRestClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">jaccard_score</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>

<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="c1"># Load a multi-label dataset from https://www.openml.org/d/40597</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="s1">&#39;yeast&#39;</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">Y</span> <span class="o">==</span> <span class="s1">&#39;TRUE&#39;</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=.</span><span class="mi">2</span><span class="p">,</span>
                                                    <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="c1"># Fit an independent logistic regression model for each class using the</span>
<span class="c1"># OneVsRestClassifier wrapper.</span>
<span class="n">base_lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
<span class="n">ovr</span> <span class="o">=</span> <span class="n">OneVsRestClassifier</span><span class="p">(</span><span class="n">base_lr</span><span class="p">)</span>
<span class="n">ovr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
<span class="n">Y_pred_ovr</span> <span class="o">=</span> <span class="n">ovr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">ovr_jaccard_score</span> <span class="o">=</span> <span class="n">jaccard_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">Y_pred_ovr</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;samples&#39;</span><span class="p">)</span>

<span class="c1"># Fit an ensemble of logistic regression classifier chains and take the</span>
<span class="c1"># take the average prediction of all the chains.</span>
<span class="n">chains</span> <span class="o">=</span> <span class="p">[</span><span class="n">ClassifierChain</span><span class="p">(</span><span class="n">base_lr</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;random&#39;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
          <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">chain</span> <span class="ow">in</span> <span class="n">chains</span><span class="p">:</span>
    <span class="n">chain</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>

<span class="n">Y_pred_chains</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">chain</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> <span class="k">for</span> <span class="n">chain</span> <span class="ow">in</span>
                          <span class="n">chains</span><span class="p">])</span>
<span class="n">chain_jaccard_scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">Y_pred_chain</span> <span class="o">&gt;=</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span>
                                      <span class="n">average</span><span class="o">=</span><span class="s1">&#39;samples&#39;</span><span class="p">)</span>
                        <span class="k">for</span> <span class="n">Y_pred_chain</span> <span class="ow">in</span> <span class="n">Y_pred_chains</span><span class="p">]</span>

<span class="n">Y_pred_ensemble</span> <span class="o">=</span> <span class="n">Y_pred_chains</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">ensemble_jaccard_score</span> <span class="o">=</span> <span class="n">jaccard_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span>
                                       <span class="n">Y_pred_ensemble</span> <span class="o">&gt;=</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span>
                                       <span class="n">average</span><span class="o">=</span><span class="s1">&#39;samples&#39;</span><span class="p">)</span>

<span class="n">model_scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">ovr_jaccard_score</span><span class="p">]</span> <span class="o">+</span> <span class="n">chain_jaccard_scores</span>
<span class="n">model_scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ensemble_jaccard_score</span><span class="p">)</span>

<span class="n">model_names</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;Independent&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 1&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 2&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 3&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 4&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 5&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 6&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 7&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 8&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 9&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Chain 10&#39;</span><span class="p">,</span>
               <span class="s1">&#39;Ensemble&#39;</span><span class="p">)</span>

<span class="n">x_pos</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">model_names</span><span class="p">))</span>

<span class="c1"># Plot the Jaccard similarity scores for the independent model, each of the</span>
<span class="c1"># chains, and the ensemble (note that the vertical axis on this plot does</span>
<span class="c1"># not begin at 0).</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Classifier Chain Ensemble Performance Comparison&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">x_pos</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">model_names</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Jaccard Similarity Score&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">model_scores</span><span class="p">)</span> <span class="o">*</span> <span class="o">.</span><span class="mi">9</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">model_scores</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.1</span><span class="p">])</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">chain_jaccard_scores</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;g&#39;</span><span class="p">]</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">model_scores</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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